Priya Jaiswal stands as a formidable figure in the world of transaction banking, bringing years of seasoned expertise in market analysis and portfolio management to the table. As corporate treasuries move away from the sluggish, batch-processed systems of the past, Jaiswal has been at the forefront of defining what a modern, digital-first financial architecture looks like. Her insights bridge the gap between legacy infrastructure and the high-velocity demands of the modern global economy, offering a roadmap for leaders navigating the shift toward continuous, real-time operations. In this discussion, we explore the convergence of real-time liquidity, embedded finance, and the practical application of artificial intelligence, examining how these forces are reshaping the very definition of corporate banking. We will look at the decline of traditional settlement models, the rise of the $13 trillion B2B embedded banking market, and the crucial role of AI in moving treasury from a back-office function to a strategic operating layer.
Retail banking has normalized instant balance updates and proactive alerts. How should corporate treasurers shift from intraday visibility to continuous, real-time cash positioning across multiple geographies? What specific opportunity costs arise for firms that stick to traditional T+ settlement models while their competitors digitize their supply chains?
The shift from intraday to continuous cash positioning is no longer an aspirational goal; it is becoming the operational floor for any enterprise that wants to remain competitive. Treasurers must move beyond the habit of checking balances at set intervals and embrace a real-time operating rhythm where “visibility” acts as a direct input for immediate action. In a world where payments can finalize in seconds, holding onto T+ settlement models creates a palpable drag on a firm’s agility, as idle cash buffers essentially become wasted capital that could have been deployed for growth or debt reduction. We are seeing a significant rise in the penalty for late interventions; when your competitors are settling cross-border and domestic transactions instantly, your stuck capital represents a lost opportunity for liquidity steering. In 2026, the traditional model of waiting for end-of-day reports will feel like trying to navigate a high-speed highway using a map that is twenty-four hours old. Firms that fail to transition will find their supply chains brittle and their ability to respond to market disruptions severely hampered compared to those using event-driven, predictive signals.
Projections suggest B2B embedded banking value could reach $13 trillion by 2030, moving financial actions into ERP and procurement systems. How can banks avoid being relegated to invisible balance-sheet utilities in this environment? What are the best practices for maintaining governance as virtual accounts and sub-ledgers proliferate?
To avoid becoming “invisible balance-sheet utilities,” banks must shift their focus from being simple transaction processors to becoming the “distribution power” behind the commercial front door. With the B2B embedded banking market projected to hit $13 trillion by 2030—a massive jump from the total $20.8 trillion expected in the overall embedded sector—banks need to ensure their services are natively integrated into the ERP and procurement systems where commercial decisions are actually made. The best practices for governance in this new landscape involve moving toward “compliance by construction,” where virtual accounts and sub-ledgers are governed by automated protocols that scale effortlessly. It is about designing cash governance that doesn’t slow down the business but rather flows alongside it, ensuring that every wallet-like balance or virtual structure remains transparent and compliant. If banks can successfully orchestrate this complex web of liquidity within the enterprise’s own workflow, they move from being a hidden utility to an indispensable partner in the client’s daily commercial life.
AI is transitioning from experimental use to providing practical value in areas like variance explanation for cash flow forecasting. What data foundations must be in place before a firm can operationalize these tools? How does AI-assisted reconciliation help manage the increased matching complexity brought by higher real-time payment volumes?
Before a firm can truly operationalize AI, it must move away from fragmented bank relationships and legacy platforms that trap information in silos; the necessary foundation is a unified data store and a modern, API-first integration layer. We are seeing that 2026 will be the year AI moves past the hype to deliver measurable value, particularly in explaining why cash forecasts shift rather than just predicting the shift itself. This variance explanation is critical for treasurers who need to justify their actions to the board in near real-time during periods of market volatility. Furthermore, as real-time payment volumes surge, the sheer number of transactions makes manual reconciliation impossible; AI-assisted reconciliation becomes the only way to manage the proliferation of virtual account structures. This technology automates the matching process at a scale and speed that humans simply cannot match, turning a traditionally labor-intensive back-office chore into a streamlined, high-speed data operation.
As cash services embed deeper into business workflows through APIs, security must move upstream and become more contextual. What are the practical steps for integrating AI-powered fraud detection directly into the payment initiation phase? How do these automated controls balance the need for speed with rigorous risk management?
Integrating security into the payment initiation phase requires a fundamental shift in how we think about the “surface area” of cash services, moving protection directly into the enterprise workflow through APIs. Practical steps include the standard adoption of AI-powered fraud detection and tokenization, which allow for real-time analysis of transaction patterns before the payment is even released. By making security contextual—meaning the system understands the normal behavior of a specific procurement or payroll flow—we can flag anomalies instantly without slowing down the vast majority of legitimate, high-speed transactions. This balance is achieved through “automated controls” that act as invisible guardrails; they provide the rigorous risk management necessary for large-scale corporate moves while maintaining the friction-less experience that modern digital commerce demands. As these services embed deeper, the goal is to ensure that the speed of the business is never compromised by the necessity of the defense.
The evolution of treasury services into a real-time operating layer transforms banks into orchestrators of enterprise liquidity. What specific metrics should leaders use to benchmark this transition in 2026? How does this shift affect the way working capital is optimized during periods of rapid global growth?
In 2026, leaders should benchmark their transition using metrics centered on data readiness, governance maturity, and the speed of liquidity steering across multiple entities and currencies. We are moving toward a model where the success of a bank is measured by its ability to act as an orchestrator, optimizing working capital in real-time to support rapid global growth without the need for excessive, idle cash reserves. This shift allows firms to be much more aggressive in their expansion strategies, as they can rely on policy-driven automation and scenario-based liquidity recommendations to move funds exactly where they are needed. It transforms the treasury from a reactive, back-office function into a proactive operating layer that actively contributes to the firm’s bottom line. In periods of rapid growth, this level of intelligence ensures that capital isn’t just sitting in a domestic rail, but is instead being put to work in the most efficient way possible across the entire global footprint.
What is your forecast for the new architecture of cash management?
My forecast is that the new architecture of cash management will cease to be a collection of disconnected banking portals and will instead become a seamless, intelligent operating layer that lives entirely within the enterprise’s native environment. In 2026, we will see the total obsolescence of batch-era constraints, replaced by a continuous flow where payments, FX, and liquidity positioning are executed as “hygiene” at the moment of action. Banks will thrive or fail based on their ability to provide “agentic” capabilities—tools that don’t just advise the treasurer but actually execute dynamic routing and automated risk controls within strict governance guardrails. The future is one where the complexity of managing $13 trillion in B2B transactions is handled by AI-driven architectures that make global liquidity as easy to manage as a local retail account. We are moving toward an era of “total visibility,” where every dollar is accounted for, protected, and optimized the very millisecond it enters the corporate ecosystem.
